
#' Adds missing elements to a vector from a default vector
#'
#' This function appends a given named vector or list with additional elements
#' from a default vector, only adding those that does not already exist in the
#' first.
#'
#' @param x,y Named vectors or lists
#'
#' @return `x` with missing values from `y` appended
#'
#' @keywords internal
#' @noRd
#'
defaults <- function(x, y) c(x, y[setdiff(names(y), names(x))])
# Remove rownames from data frames and matrices
unrowname <- function(x) {
  if (is.data.frame(x)) {
    attr(x, "row.names") <- .set_row_names(.row_names_info(x, 2L))
  } else if (is.matrix(x)) {
    dimnames(x)[1] <- list(NULL)
  } else {
    abort("Can only remove rownames from data.frame and matrix objects")
  }
  x
}
#' Rename elements in a list, data.frame or vector
#'
#' This is akin to `dplyr::rename` and `plyr::rename`. It renames elements given
#' as names in the `replace` vector to the values in the `replace` vector
#' without touching elements not referenced.
#'
#' @param x A data.frame or a named vector or list
#' @param replace A named character vector. The names identifies the elements in
#' `x` that should be renamed and the values gives the new names.
#'
#' @return `x`, with new names according to `replace`
#'
#' @keywords internal
#' @noRd
#'
rename <- function(x, replace) {
  current_names <- names(x)
  old_names <- names(replace)
  missing_names <- setdiff(old_names, current_names)
  if (length(missing_names) > 0) {
    replace <- replace[!old_names %in% missing_names]
    old_names <- names(replace)
  }
  names(x)[match(old_names, current_names)] <- as.vector(replace)
  x
}
# Adapted from plyr:::id_vars
# Create a unique id for elements in a single vector
id_var <- function(x, drop = FALSE) {
  if (length(x) == 0) {
    id <- integer()
    n = 0L
  } else if (!is.null(attr(x, "n")) && !drop) {
    return(x)
  } else if (is.factor(x) && !drop) {
    x <- addNA(x, ifany = TRUE)
    id <- as.integer(x)
    n <- length(levels(x))
  } else {
    levels <- sort(unique(x), na.last = TRUE)
    id <- match(x, levels)
    n <- max(id)
  }
  attr(id, "n") <- n
  id
}
#' Create an unique integer id for each unique row in a data.frame
#'
#' Properties:
#' - `order(id)` is equivalent to `do.call(order, df)`
#' - rows containing the same data have the same value
#' - if `drop = FALSE` then room for all possibilites
#'
#' @param .variables list of variables
#' @param drop Should unused factor levels be dropped?
#'
#' @return An integer vector with attribute `n` giving the total number of
#' possible unique rows
#'
#' @keywords internal
#' @noRd
#'
id <- function(.variables, drop = FALSE) {
  nrows <- NULL
  if (is.data.frame(.variables)) {
    nrows <- nrow(.variables)
    .variables <- unclass(.variables)
  }
  lengths <- vapply(.variables, length, integer(1))
  .variables <- .variables[lengths != 0]
  if (length(.variables) == 0) {
    n <- nrows %||% 0L
    id <- seq_len(n)
    attr(id, "n") <- n
    return(id)
  }
  if (length(.variables) == 1) {
    return(id_var(.variables[[1]], drop = drop))
  }
  ids <- rev(lapply(.variables, id_var, drop = drop))
  p <- length(ids)
  ndistinct <- vapply(ids, attr, "n", FUN.VALUE = numeric(1), USE.NAMES = FALSE)
  n <- prod(ndistinct)
  if (n > 2^31) {
    char_id <- do.call("paste", c(ids, sep = "\r"))
    res <- match(char_id, unique(char_id))
  }
  else {
    combs <- c(1, cumprod(ndistinct[-p]))
    mat <- do.call("cbind", ids)
    res <- c((mat - 1L) %*% combs + 1L)
  }
  if (drop) {
    id_var(res, drop = TRUE)
  }
  else {
    res <- as.integer(res)
    attr(res, "n") <- n
    res
  }
}
#' Count number of occurences for each unique combination of variables
#'
#' Each unique combination of the variables in `df` given by `vars` will be
#' identified and their occurences counted. If `wt_var` is given the counts will
#' be weighted by the values in this column.
#'
#' @param df A data.frame
#' @param vars A vector of column names. If `NULL` all columns in `df` will be
#' used
#' @param wt_var The name of a column to use as weight
#'
#' @return A data.frame with the unique combinations counted along with a `n`
#' column giving the counts
#'
#' @keywords internal
#' @noRd
#'
count <- function(df, vars = NULL, wt_var = NULL) {
  df2 <- if (is.null(vars)) df else df[vars]
  id <- id(df2, drop = TRUE)
  u_id <- !duplicated(id)
  labels <- df2[u_id, , drop = FALSE]
  labels <- labels[order(id[u_id]), , drop = FALSE]
  if (is.null(wt_var)) {
    freq <- tabulate(id, attr(id, "n"))
  } else {
    wt <- .subset2(df, wt_var)
    freq <- vapply(split(wt, id), sum, numeric(1))
  }
  new_data_frame(c(as.list(labels), list(n = freq)))
}
# Adapted from plyr::join.keys
# Create a shared unique id across two data frames such that common variable
# combinations in the two data frames gets the same id
join_keys <- function(x, y, by) {
  joint <- rbind_dfs(list(x[by], y[by]))
  keys <- id(joint, drop = TRUE)
  n_x <- nrow(x)
  n_y <- nrow(y)
  list(x = keys[seq_len(n_x)], y = keys[n_x + seq_len(n_y)],
       n = attr(keys, "n"))
}
#' Replace specified values with new values, in a factor or character vector
#'
#' An easy to use substitution of elements in a string-like vector (character or
#' factor). If `x` is a character vector the matching elements will be replaced
#' directly and if `x` is a factor the matching levels will be replaced
#'
#' @param x A character or factor vector
#' @param replace A named character vector with the names corresponding to the
#' elements to replace and the values giving the replacement.
#'
#' @return A vector of the same class as `x` with the given values replaced
#'
#' @keywords internal
#' @noRd
#'
revalue <- function(x, replace) {
  if (is.character(x)) {
    replace <- replace[names(replace) %in% x]
    if (length(replace) == 0) return(x)
    x[match(names(replace), x)] <- replace
  } else if (is.factor(x)) {
    lev <- levels(x)
    replace <- replace[names(replace) %in% lev]
    if (length(replace) == 0) return(x)
    lev[match(names(replace), lev)] <- replace
    levels(x) <- lev
  } else if (!is.null(x)) {
    abort("x is not a factor or character vector")
  }
  x
}
# Iterate through a formula and return a quoted version
simplify_formula <- function(x) {
  if (length(x) == 2 && x[[1]] == as.name("~")) {
    return(simplify(x[[2]]))
  }
  if (length(x) < 3)
    return(list(x))
  op <- x[[1]]
  a <- x[[2]]
  b <- x[[3]]
  if (op == as.name("+") || op == as.name("*") || op ==
      as.name("~")) {
    c(simplify(a), simplify(b))
  }
  else if (op == as.name("-")) {
    c(simplify(a), bquote(-.(x), list(x = simplify(b))))
  }
  else {
    list(x)
  }
}
#' Create a quoted version of x
#'
#' This function captures the special meaning of formulas in the context of
#' facets in ggplot2, where `+` have special meaning. It works as
#' `plyr::as.quoted` but only for the special cases of `character`, `call`, and
#' `formula` input as these are the only situations relevant for ggplot2.
#'
#' @param x A formula, string, or call to be quoted
#' @param env The environment to a attach to the quoted expression.
#'
#' @keywords internal
#' @noRd
#'
as.quoted <- function(x, env = parent.frame()) {
  x <- if (is.character(x)) {
    lapply(x, function(x) parse(text = x)[[1]])
  } else if (is.formula(x)) {
    simplify_formula(x)
  } else if (is.call(x)) {
    as.list(x)[-1]
  } else {
    abort("Only knows how to quote characters, calls, and formula")
  }
  attributes(x) <- list(env = env, class = 'quoted')
  x
}
# round a number to a given precision
round_any <- function(x, accuracy, f = round) {
  if (!is.numeric(x)) abort("`x` must be numeric")
  f(x/accuracy) * accuracy
}
#' Bind data frames together by common column names
#'
#' This function is akin to `plyr::rbind.fill`, `dplyr::bind_rows`, and
#' `data.table::rbindlist`. It takes data frames in a list and stacks them on
#' top of each other, filling out values with `NA` if the column is missing from
#' a data.frame
#'
#' @param dfs A list of data frames
#'
#' @return A data.frame with the union of all columns from the data frames given
#' in `dfs`
#'
#' @keywords internal
#' @noRd
#'
rbind_dfs <- function(dfs) {
  out <- list()
  columns <- unique(unlist(lapply(dfs, names)))
  nrows <- vapply(dfs, .row_names_info, integer(1), type = 2L)
  total <- sum(nrows)
  if (length(columns) == 0) return(new_data_frame(list(), total))
  allocated <- rep(FALSE, length(columns))
  names(allocated) <- columns
  col_levels <- list()
  ord_levels <- list()
  for (df in dfs) {
    new_columns <- intersect(names(df), columns[!allocated])
    for (col in new_columns) {
      if (is.factor(df[[col]])) {
        all_ordered <- all(vapply(dfs, function(df) {
          val <- .subset2(df, col)
          is.null(val) || is.ordered(val)
        }, logical(1)))
        all_factors <- all(vapply(dfs, function(df) {
          val <- .subset2(df, col)
          is.null(val) || is.factor(val)
        }, logical(1)))
        if (all_ordered) {
          ord_levels[[col]] <- unique(unlist(lapply(dfs, function(df) levels(.subset2(df, col)))))
        } else if (all_factors) {
          col_levels[[col]] <- unique(unlist(lapply(dfs, function(df) levels(.subset2(df, col)))))
        }
        out[[col]] <- rep(NA_character_, total)
      } else {
        out[[col]] <- rep(.subset2(df, col)[1][NA], total)
      }
    }
    allocated[new_columns] <- TRUE
    if (all(allocated)) break
  }
  is_date <- lapply(out, inherits, 'Date')
  is_time <- lapply(out, inherits, 'POSIXct')
  pos <- c(cumsum(nrows) - nrows + 1)
  for (i in seq_along(dfs)) {
    df <- dfs[[i]]
    rng <- seq(pos[i], length.out = nrows[i])
    for (col in names(df)) {
      date_col <- inherits(df[[col]], 'Date')
      time_col <- inherits(df[[col]], 'POSIXct')
      if (is_date[[col]] && !date_col) {
        out[[col]][rng] <- as.Date(
          unclass(df[[col]]),
          origin = ggplot_global$date_origin
        )
      } else if (is_time[[col]] && !time_col) {
        out[[col]][rng] <- as.POSIXct(
          unclass(df[[col]]),
          origin = ggplot_global$time_origin
        )
      } else if (date_col || time_col || inherits(df[[col]], 'factor')) {
        out[[col]][rng] <- as.character(df[[col]])
      } else {
        out[[col]][rng] <- df[[col]]
      }
    }
  }
  for (col in names(ord_levels)) {
    out[[col]] <- ordered(out[[col]], levels = ord_levels[[col]])
  }
  for (col in names(col_levels)) {
    out[[col]] <- factor(out[[col]], levels = col_levels[[col]])
  }
  attributes(out) <- list(
    class = "data.frame",
    names = names(out),
    row.names = .set_row_names(total)
  )
  out
}
#' Apply function to unique subsets of a data.frame
#'
#' This function is akin to `plyr::ddply`. It takes a single data.frame,
#' splits it by the unique combinations of the columns given in `by`, apply a
#' function to each split, and then reassembles the results into a sigle
#' data.frame again.
#'
#' @param df A data.frame
#' @param by A character vector of column names to split by
#' @param fun A function to apply to each split
#' @param ... Further arguments to `fun`
#' @param drop Should unused factor levels in the columns given in `by` be
#' dropped.
#'
#' @return A data.frame if the result of `fun` does not include the columns
#' given in `by` these will be prepended to the result.
#'
#' @keywords internal
#' @noRd
dapply <- function(df, by, fun, ..., drop = TRUE) {
  grouping_cols <- .subset(df, by)
  fallback_order <- unique(c(by, names(df)))
  apply_fun <- function(x) {
    res <- fun(x, ...)
    if (is.null(res)) return(res)
    if (length(res) == 0) return(new_data_frame())
    vars <- lapply(setNames(by, by), function(col) .subset2(x, col)[1])
    if (is.matrix(res)) res <- split_matrix(res)
    if (is.null(names(res))) names(res) <- paste0("V", seq_along(res))
    if (all(by %in% names(res))) return(new_data_frame(unclass(res)))
    res <- modify_list(unclass(vars), unclass(res))
    new_data_frame(res[intersect(c(fallback_order, names(res)), names(res))])
  }

  # Shortcut when only one group
  if (all(vapply(grouping_cols, single_value, logical(1)))) {
    return(apply_fun(df))
  }

  ids <- id(grouping_cols, drop = drop)
  group_rows <- split_with_index(seq_len(nrow(df)), ids)
  rbind_dfs(lapply(seq_along(group_rows), function(i) {
    cur_data <- df_rows(df, group_rows[[i]])
    apply_fun(cur_data)
  }))
}

single_value <- function(x, ...) {
  UseMethod("single_value")
}
#' @export
single_value.default <- function(x, ...) {
  # This is set by id() used in creating the grouping var
  identical(attr(x, "n"), 1L)
}
#' @export
single_value.factor <- function(x, ...) {
  # Panels are encoded as factor numbers and can never be missing (NA)
  identical(levels(x), "1")
}
